Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization

Jason K D Chan; Eric A Chadwick; Daisuke Taniguchi; Mohammadali Ahmadipour; Takaya Suzuki; David Romero; Cristina Amon; Thomas K Waddell; Golnaz Karoubi; Aimy Bazylak

doi:10.3389/fbioe.2022.891407

Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization

Front Bioeng Biotechnol. 2022 Apr 27:10:891407. doi: 10.3389/fbioe.2022.891407. eCollection 2022.

Authors

Jason K D Chan¹, Eric A Chadwick¹, Daisuke Taniguchi², Mohammadali Ahmadipour^{2

3}, Takaya Suzuki⁴, David Romero¹, Cristina Amon^{1

3}, Thomas K Waddell^{2

3

5}, Golnaz Karoubi^{1

2

6}, Aimy Bazylak¹

Affiliations

¹ Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
² Latner Thoracic Surgery Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada.
³ Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada.
⁴ Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
⁵ Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
⁶ Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.

Abstract

We created a transient computational fluid dynamics model featuring a particle deposition probability function that incorporates inertia to quantify the transport and deposition of cells in mouse lung vasculature for the re-endothelialization of the acellular organ. Our novel inertial algorithm demonstrated a 73% reduction in cell seeding efficiency error compared to two established particle deposition algorithms when validated with experiments based on common clinical practices. We enhanced the uniformity of cell distributions in the lung vasculature by increasing the injection flow rate from 3.81 ml/min to 9.40 ml/min. As a result, the cell seeding efficiency increased in both the numerical and experimental results by 42 and 66%, respectively.

Keywords: cell seeding; computational fluid dynamics; inertia; lung regeneration; lung tissue engineering; re-endothelialization.